Sujong Kim

Changes of gene expression profiles during neuronal differentiation of central nervous system precursors treated with ascorbic acid

Ascorbic acid (AA) has been shown to increase the yield of dopaminergic (DA) neurons derived from basic fibroblast growth factor (bFGF)‐expanded mesencephalic precursors. To understand the molecular mechanisms underlying this phenomenon, we used cDNA microarray analysis to examine differential expression of neuronal genes following AA treatment. The putative precursor cells were isolated from E13 rat ventral mesencephalons and expanded in the presence of bFGF. Cells were incubated in mitogen‐free media supplemented with 200 μM AA or were left untreated as a control, and total RNA was isolated at different time points (expansion stage and 1, 3, and 6 days after induction of differentiation) and subjected to cDNA microarray analysis. Differentiation was evaluated by Western blot analysis and immunocytochemistry of neuron‐specific markers. AA treatment of the mesencephalic precursors increased the expression of neuronal (MAP2) and astrocytic (glial fibrillary acidic protein) markers and the percentage of tyrosine hydroxylase (TH)‐positive cells. The microarray analysis revealed that 12 known genes were up‐regulated and 20 known genes were down‐regulated in expansion‐stage AA‐treated cells. Six days after the induction of differentiation, AA‐treated cells showed up‐regulation of 48 known genes and down‐regulation of 5 known genes. Our results identified several proteins, such as transferrin, S‐100, and somatostatin, as being differentially regulated in AA‐treated mesencephalic precursors. This novel result may lead to a better understanding of the molecular mechanisms underlying the AA‐induced differentiation of mesencephalic precursors into DA neurons and may form the basis for improved DA neuronal production for treatment of Parkinsons disease patients.

Classification of gene functions using support vector machine for time-course gene expression data

Since most biological systems are developmental and dynamic, time-course gene expression profiles provide an important characterization of gene functions. Assigning functions for genes with unknown functions based on time-course gene expressions is an important task in functional genomics. Recently, various methods have been proposed for the classification of gene functions based on time-course gene expression data. In this paper, we consider the classification of gene functions from functional data analysis viewpoint, where a functional support vector machine is adopted. The functional support vector machine can model temporal effects of time-course gene expression data by incorporating the coefficients as well as the basis matrix obtained from a finite expansion of gene expressions on a set of basis functions. We apply the functional support vector machine to both real microarray and simulated data. Our results indicate that the functional support vector machine is effective in discriminating gene functions of time-course gene expressions with predefined functions. The method also provides valuable functional information about interactions between genes and allows the assignment of new functions to genes with unknown functions.

Structured polychotomous machine diagnosis of multiple cancer types using gene expression

MOTIVATION The problem of class prediction has received a tremendous amount of attention in the literature recently. In the context of DNA microarrays, where the task is to classify and predict the diagnostic category of a sample on the basis of its gene expression profile, a problem of particular importance is the diagnosis of cancer type based on microarray data. One method of classification which has been very successful in cancer diagnosis is the support vector machine (SVM). The latter has been shown (through simulations) to be superior in comparison with other methods, such as classical discriminant analysis, however, SVM suffers from the drawback that the solution is implicit and therefore is difficult to interpret. In order to remedy this difficulty, an analysis of variance decomposition using structured kernels is proposed and is referred to as the structured polychotomous machine. This technique utilizes Newton-Raphson to find estimates of coefficients followed by the Rao and Wald tests, respectively, for addition and deletion of import vectors. RESULTS The proposed method is applied to microarray data and simulation data. The major breakthrough of our method is efficiency in that only a minimal number of genes that accurately predict the classes are selected. It has been verified that the selected genes serve as legitimate markers for cancer classification from a biological point of view. AVAILABILITY All source codes used are available on request from the authors.

Selecting marker genes for cancer classification using supervised weighted kernel clustering and the support vector machine

Due to recent interest in the analysis of DNA microarray data, new methods have been considered and developed in the area of statistical classification. In particular, according to the gene expression profile of existing data, the goal is to classify the sample into a relevant diagnostic category. However, when classifying outcomes into certain cancer types, it is often the case that some genes are not important, while some genes are more important than others. A novel algorithm is presented for selecting such relevant genes referred to as marker genes for cancer classification. This algorithm is based on the Support Vector Machine (SVM) and Supervised Weighted Kernel Clustering (SWKC). To investigate the performance of this algorithm, the methods were applied to a simulated data set and some real data sets. For comparison, some other well-known methods such as Prediction Analysis of Microarrays (PAM), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and a Structured Polychotomous Machine (SPM) were considered. The experimental results indicate that the proposed SWKC/SVM algorithm is conceptually much simpler and performs more efficiently than other existing methods used in identifying marker genes for cancer classification. Furthermore, the SWKC/SVM algorithm has the advantage that it requires much less computing time compared with the other existing methods.

A permutation-based multiple testing method for time-course microarray experiments

BackgroundTime-course microarray experiments are widely used to study the temporal profiles of gene expression. Storey et al. (2005) developed a method for analyzing time-course microarray studies that can be applied to discovering genes whose expression trajectories change over time within a single biological group, or those that follow different time trajectories among multiple groups. They estimated the expression trajectories of each gene using natural cubic splines under the null (no time-course) and alternative (time-course) hypotheses, and used a goodness of fit test statistic to quantify the discrepancy. The null distribution of the statistic was approximated through a bootstrap method. Gene expression levels in microarray data are often complicatedly correlated. An accurate type I error control adjusting for multiple testing requires the joint null distribution of test statistics for a large number of genes. For this purpose, permutation methods have been widely used because of computational ease and their intuitive interpretation.ResultsIn this paper, we propose a permutation-based multiple testing procedure based on the test statistic used by Storey et al. (2005). We also propose an efficient computation algorithm. Extensive simulations are conducted to investigate the performance of the permutation-based multiple testing procedure. The application of the proposed method is illustrated using the Caenorhabditis elegans dauer developmental data.ConclusionOur method is computationally efficient and applicable for identifying genes whose expression levels are time-dependent in a single biological group and for identifying the genes for which the time-profile depends on the group in a multi-group setting.

Transcriptional profiling in human HaCaT keratinocytes in response to kaempferol and identification of potential transcription factors for regulating differential gene expression

Kaempferol is the major flavonol in green tea and exhibits many biomedically useful properties such as antioxidative, cytoprotective and anti-apoptotic activities. To elucidate its effects on the skin, we investigated the transcriptional profiles of kaempferol-treated HaCaT cells using cDNA microarray analysis and identified 147 transcripts that exhibited significant changes in expression. Of these, 18 were up-regulated and 129 were down-regulated. These transcripts were then classified into 12 categories according to their functional roles: cell adhesion/cytoskeleton, cell cycle, redox homeostasis, immune/defense responses, metabolism, protein biosynthesis/modification, intracellular transport, RNA processing, DNA modification/ replication, regulation of transcription, signal transduction and transport. We then analyzed the promoter sequences of differentially-regulated genes and identified over-represented regulatory sites and candidate transcription factors (TFs) for gene regulation by kaempferol. These included c-REL, SAP-1, Ahr-ARNT, Nrf-2, Elk-1, SPI-B, NF-κB and p65. In addition, we validated the microarray results and promoter analyses using conventional methods such as real-time PCR and ELISA-based transcription factor assay. Our microarray analysis has provided useful information for determining the genetic regulatory network affected by kaempferol, and this approach will be useful for elucidating gene-phytochemical interactions.

New normalization methods using support vector machine quantile regression approach in microarray analysis

There are many sources of systematic variations in cDNA microarray experiments which affect the measured gene expression levels. Print-tip lowess normalization is widely used in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. However, print-tip lowess normalization performs poorly in situations where error variability for each gene is heterogeneous over intensity ranges. We first develop support vector machine quantile regression (SVMQR) by extending support vector machine regression (SVMR) for the estimation of linear and nonlinear quantile regressions, and then propose some new print-tip normalization methods based on SVMR and SVMQR. We apply our proposed normalization methods to previous cDNA microarray data of apolipoprotein AI-knockout (apoAI-KO) mice, diet-induced obese mice, and genistein-fed obese mice. From our comparative analyses, we find that our proposed methods perform better than the existing print-tip lowess normalization method.

Ethnic differences in objective and subjective skin irritation response: an international study

Due to global marketing in the cosmetics industry, it is important to assess ethnic population susceptibility when evaluating the safety of cosmetic products or chemicals.

Informative transcription factor selection using support vector machine-based generalized approximate cross validation criteria

The genetic regulatory mechanism plays a pivotal role in many biological processes ranging from development to survival. The identification of the common transcription factor binding sites (TFBSs) from a set of known co-regulated gene promoters and the identification of genes that are regulated by the transcription factor (TF) that have important roles in a particular biological function will advance our understanding of the interaction among the co-regulated genes and intricate genetic regulatory mechanism underlying this function. To identify the common TFBSs from a set of known co-regulated gene promoters and classify genes that are regulated by TFs, the new approaches using Support Vector Machine (SVM)-based Generalized Approximate Cross Validation (GACV) criteria are proposed. Two variable selection methods are considered for Recursive Feature Elimination (RFE) and Recursive Feature Addition (RFA). Performances of the proposed methods are compared with the existing SVM-based criteria, Logistic Regression Analysis (LRA), Logic Regression (LR), and Decision Tree (DT) methods by using both two real TF target genes data and the simulated data. In terms of test error rates, the proposed methods perform better than the existing methods.

Analysis of Survival Data with Group Lasso

Identification of influential genes and clinical covariates on the survival of patients is crucial because it can lead us to better understanding of underlying mechanism of diseases and better prediction models. Most of variable selection methods in penalized Cox models cannot deal properly with categorical variables such as gender and family history. The group lasso penalty can combine clinical and genomic covariates effectively. In this article, we introduce an optimization algorithm for Cox regression with group lasso penalty. We compare our method with other methods on simulated and real microarray data sets.